



'''simple docstring'''






import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def  lowerCamelCase__			(		_A					,     _A							):
  a		:   Tuple	  =	old_name

  if "patch_embed" in old_name:
    a  ,      a  ,      a		:   Optional[Any]	  =	old_name.split('.'							)

    if layer == "0":
      a		:   List[str]	  =	old_name.replace('0'					,     'convolution1'							)
    elif layer == "1":
      a		:   Union[str, Any]	  =	old_name.replace('1'					,     'batchnorm_before'							)
    elif layer == "3":
      a		:   str	  =	old_name.replace('3'					,     'convolution2'							)
    else:
      a		:   Optional[int]	  =	old_name.replace('4'					,     'batchnorm_after'							)

  if "network" in old_name and re.search(r'\d\.\d'					,     _A							):
    a		:   Union[str, Any]	  =	r'\b\d{2}\b'
    if bool(re.search(_A					,     _A							)							):
      a		:   List[str]	  =	re.search(r'\d\.\d\d.'					,     _A							).group()
    else:
      a		:   int	  =	re.search(r'\d\.\d.'					,     _A							).group()
    if int(match[0]							) < 6:
      a		:   Dict	  =	old_name.replace(_A					,     ''							)
      a		:   List[Any]	  =	trimmed_name.replace('network'					,     match[0] + '.meta4D_layers.blocks.' + match[2:-1]							)
      a		:   Any	  =	'intermediate_stages.' + trimmed_name
    else:
      a		:   str	  =	old_name.replace(_A					,     ''							)
      if int(match[2]							) < num_meta4D_last_stage:
        a		:   Union[str, Any]	  =	trimmed_name.replace('network'					,     'meta4D_layers.blocks.' + match[2]							)
      else:
        a		:   str	  =	str(int(match[2]							) - num_meta4D_last_stage							)
        a		:   List[Any]	  =	trimmed_name.replace('network'					,     'meta3D_layers.blocks.' + layer_index							)
        if "norm1" in old_name:
          a		:   Dict	  =	trimmed_name.replace('norm1'					,     'layernorm1'							)
        elif "norm2" in old_name:
          a		:   Union[str, Any]	  =	trimmed_name.replace('norm2'					,     'layernorm2'							)
        elif "fc1" in old_name:
          a		:   Optional[int]	  =	trimmed_name.replace('fc1'					,     'linear_in'							)
        elif "fc2" in old_name:
          a		:   Union[str, Any]	  =	trimmed_name.replace('fc2'					,     'linear_out'							)

      a		:   Optional[int]	  =	'last_stage.' + trimmed_name

  elif "network" in old_name and re.search(r'.\d.'					,     _A							):
    a		:   List[Any]	  =	old_name.replace('network'					,     'intermediate_stages'							)

  if "fc" in new_name:
    a		:   Any	  =	new_name.replace('fc'					,     'convolution'							)
  elif ("norm1" in new_name) and ("layernorm1" not in new_name):
    a		:   List[Any]	  =	new_name.replace('norm1'					,     'batchnorm_before'							)
  elif ("norm2" in new_name) and ("layernorm2" not in new_name):
    a		:   Optional[int]	  =	new_name.replace('norm2'					,     'batchnorm_after'							)
  if "proj" in new_name:
    a		:   Any	  =	new_name.replace('proj'					,     'projection'							)
  if "dist_head" in new_name:
    a		:   Dict	  =	new_name.replace('dist_head'					,     'distillation_classifier'							)
  elif "head" in new_name:
    a		:   List[Any]	  =	new_name.replace('head'					,     'classifier'							)
  elif "patch_embed" in new_name:
    a		:   str	  =	'efficientformer.' + new_name
  elif new_name == "norm.weight" or new_name == "norm.bias":
    a		:   List[Any]	  =	new_name.replace('norm'					,     'layernorm'							)
    a		:   Dict	  =	'efficientformer.' + new_name
  else:
    a		:   Optional[Any]	  =	'efficientformer.encoder.' + new_name

  return new_name
def  lowerCamelCase__			(		_A					,     _A							):
  for key in checkpoint.copy().keys():
    a		:   Union[str, Any]	  =	checkpoint.pop(_A							)
    a		:   Optional[int]	  =	val

  return checkpoint
def  lowerCamelCase__			(		):
  a		:   List[str]	  =	'http://images.cocodataset.org/val2017/000000039769.jpg'
  a		:   Any	  =	Image.open(requests.get(_A					,     stream=_A							).raw							)

  return image
def  lowerCamelCase__			(		_A					,     _A					,     _A					,     _A							):
  a		:   Optional[Any]	  =	torch.load(_A					,     map_location='cpu'							)['model']
  a		:   List[Any]	  =	EfficientFormerConfig.from_json_file(_A							)
  a		:   Any	  =	EfficientFormerForImageClassificationWithTeacher(_A							)
  a		:   List[str]	  =	'_'.join(checkpoint_path.split('/'							)[-1].split('.'							)[0].split('_'							)[:-1]							)

  a		:   int	  =	config.depths[-1] - config.num_metaad_blocks + 1
  a		:   List[Any]	  =	convert_torch_checkpoint(_A					,     _A							)

  model.load_state_dict(_A							)
  model.eval()

  a		:   List[str]	  =	{
      'bilinear': PILImageResampling.BILINEAR,
      'bicubic': PILImageResampling.BICUBIC,
      'nearest': PILImageResampling.NEAREST,
  }

  # prepare image
  a		:   str	  =	prepare_img()
  a		:   Optional[Any]	  =	256
  a		:   Dict	  =	224
  a		:   Any	  =	EfficientFormerImageProcessor(
      size={'shortest_edge': image_size}					,     crop_size={'height': crop_size, 'width': crop_size}					,     resample=pillow_resamplings['bicubic']					,     )
  a		:   Union[str, Any]	  =	processor(images=_A					,     return_tensors='pt'							).pixel_values

  # original processing pipeline
  a		:   Optional[int]	  =	Compose(
      [
          Resize(_A					,     interpolation=pillow_resamplings['bicubic']							),
          CenterCrop(_A							),
          ToTensor(),
          Normalize(_A					,     _A							),
      ]							)
  a		:   Optional[Any]	  =	image_transforms(_A							).unsqueeze(0							)

  assert torch.allclose(_A					,     _A							)

  a		:   Tuple	  =	model(_A							)
  a		:   Tuple	  =	outputs.logits

  a		:   Any	  =	(1, 1000)

  if "l1" in model_name:
    a		:   str	  =	torch.Tensor(
        [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]							)
    assert torch.allclose(logits[0, :10]					,     _A					,     atol=1E-3							)
    assert logits.shape == expected_shape
  elif "l3" in model_name:
    a		:   Optional[Any]	  =	torch.Tensor(
        [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]							)
    assert torch.allclose(logits[0, :10]					,     _A					,     atol=1E-3							)
    assert logits.shape == expected_shape
  elif "l7" in model_name:
    a		:   Any	  =	torch.Tensor(
        [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]							)
    assert logits.shape == expected_shape
  else:
    raise ValueError(
        f"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""							)

  # Save Checkpoints
  Path(_A							).mkdir(exist_ok=_A							)
  model.save_pretrained(_A							)
  print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""							)
  processor.save_pretrained(_A							)
  print(f"""Processor successfuly saved at {pytorch_dump_path}"""							)

  if push_to_hub:
    print('Pushing model to the hub...'							)

    model.push_to_hub(
        repo_id=f"""Bearnardd/{pytorch_dump_path}"""					,     commit_message='Add model'					,     use_temp_dir=_A					,     )
    processor.push_to_hub(
        repo_id=f"""Bearnardd/{pytorch_dump_path}"""					,     commit_message='Add image processor'					,     use_temp_dir=_A					,     )


if __name__ == "__main__":
     lowerCAmelCase:   int					=		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--pytorch_model_path',
         default=None,
         type=str,
         required=True,
         help='Path to EfficientFormer pytorch checkpoint.',
     )
     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The json file for EfficientFormer model config.',
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )

     parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
     parser.add_argument(
         '--no-push_to_hub',
         dest='push_to_hub',
         action='store_false',
         help='Do not push model and image processor to the hub',
     )
     parser.set_defaults(push_to_hub=True)

     lowerCAmelCase:   List[Any]					=		parser.parse_args()
     convert_efficientformer_checkpoint(
         checkpoint_path=args.pytorch_model_path,
         efficientformer_config_file=args.config_file,
         pytorch_dump_path=args.pytorch_dump_path,
         push_to_hub=args.push_to_hub,
     )




'''simple docstring'''






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    int		,     __snake_case					:    Callable		,     __snake_case					:    Optional[Features] = None		,     __snake_case					:    str = None		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    Optional[dict] = None		,     __snake_case					:    Optional[int] = None		,     **__snake_case					:    Optional[int]		,     ):
      super().__init__(
          features=__snake_case		,     cache_dir=__snake_case		,     keep_in_memory=__snake_case		,     streaming=__snake_case		,     num_proc=__snake_case		,     **__snake_case		,     )
      a		:   List[Any]	  =	Generator(
          cache_dir=__snake_case		,     features=__snake_case		,     generator=__snake_case		,     gen_kwargs=__snake_case		,     **__snake_case		,     )







    def      lowercase_			( self					:    Any      ):
      # Build iterable dataset
      if self.streaming:
        a		:   Optional[int]	  =	self.builder.as_streaming_dataset(split='train'      )
      # Build regular (map-style) dataset
      else:
        a		:   List[Any]	  =	None
        a		:   Any	  =	None
        a		:   Optional[int]	  =	None
        a		:   List[str]	  =	None

        self.builder.download_and_prepare(
            download_config=__snake_case		,     download_mode=__snake_case		,     verification_mode=__snake_case		,     base_path=__snake_case		,     num_proc=self.num_proc		,     )
        a		:   Dict	  =	self.builder.as_dataset(
            split='train'		,     verification_mode=__snake_case		,     in_memory=self.keep_in_memory      )
      return dataset